OpenRaiser/Intern-Atlas huggingface dataset:
An Implementation of Back-Propagation Learning on GF11, a Large SIMD Parallel Computer
Venue: arXiv cs.LG 2018
Year: 2018

Current connectionist simulations require huge computational resources. We describe a neural network simulator for the IBM GF11, an experimental SIMD machine with 566 processors and a peak arithmetic performance of 11 Gigaflops. We present our parallel implementation of the backpropagation learning algorithm, techniques for increasing efficiency, performance measurements on the NetTalk text-to-speech benchmark, and a performance model for the simulator. Our simulator currently runs the back-propagation learning algorithm at 900 million connections per second, where each "connection per second" includes both a forward and backward pass. This figure was obtained on the machine when only 356 processors were working; with all 566 processors operational, our simulation will run at over one billion connections per second. We conclude that the GF11 is well-suited to neural network simulation, and we analyze our use of the machine to determine which features are the most important for high performance.
A Study on the Use of Eye Tracking to Adapt Gameplay and Procedural Content Generation in First-Person Shooter Games
Venue: arXiv cs.HC 2018
Year: 2018

This paper studies the use of eye tracking in a First-Person Shooter (FPS) game as a~mechanism to: (1) control the attention of the player's avatar according to the attention deployed by the player, and (2) guide the gameplay and game's procedural content generation, accordingly. This results in a more natural use of eye tracking in comparison to a use in which the eye tracker directly substitutes control input devices, such as gamepads. The study was conducted on a custom endless runner FPS, Zombie Runner, using an affordable eye tracker. Evaluation sessions showed that the proposed use of eye tracking provides a more challenging and immersive experience to the player, when compared to its absence. However, a strong correlation between eye tracker calibration problems and player's overall experience was found. This means that eye tracking technology still needs to evolve but also means that once technology gets mature enough players are expected to benefit greatly from the inclusion of eye tracking in their gaming experience.
Neural Style Transfer for Audio Spectograms
Venue: arXiv cs.SD 2018
Year: 2018

There has been fascinating work on creating artistic transformations of images by Gatys. This was revolutionary in how we can in some sense alter the 'style' of an image while generally preserving its 'content'. In our work, we present a method for creating new sounds using a similar approach, treating it as a style-transfer problem, starting from a random-noise input signal and iteratively using back-propagation to optimize the sound to conform to filter-outputs from a pre-trained neural architecture of interest. For demonstration, we investigate two different tasks, resulting in bandwidth expansion/compression, and timbral transfer from singing voice to musical instruments. A feature of our method is that a single architecture can generate these different audio-style-transfer types using the same set of parameters which otherwise require different complex hand-tuned diverse signal processing pipelines.
Estimation in the spiked Wigner model: A short proof of the replica formula
Venue: arXiv cs.IT 2018
Year: 2018

We consider the problem of estimating a rank-one perturbation of a Wigner matrix in a setting of low signal-to-noise ratio. This serves as a simple model for principal component analysis in high dimensions. The mutual information per variable between the spike and the observed matrix, or equivalently, the normalized Kullback-Leibler divergence between the planted and null models are known to converge to the so-called {\em replica-symmetric} formula, the properties of which determine the fundamental limits of estimation in this model. We provide in this note a short and transparent proof of this formula, based on simple executions of Gaussian interpolations and standard concentration-of-measure arguments. The \emph{Franz-Parisi potential}, that is, the free entropy at a fixed overlap, plays an important role in our proof. Furthermore, our proof can be generalized straightforwardly to spiked tensor models of even order.
Learning $3$D-FilterMap for Deep Convolutional Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

We present a novel and compact architecture for deep Convolutional Neural Networks (CNNs) in this paper, termed $3$D-FilterMap Convolutional Neural Networks ($3$D-FM-CNNs). The convolution layer of $3$D-FM-CNN learns a compact representation of the filters, named $3$D-FilterMap, instead of a set of independent filters in the conventional convolution layer. The filters are extracted from the $3$D-FilterMap as overlapping $3$D submatrics with weight sharing among nearby filters, and these filters are convolved with the input to generate the output of the convolution layer for $3$D-FM-CNN. Due to the weight sharing scheme, the parameter size of the $3$D-FilterMap is much smaller than that of the filters to be learned in the conventional convolution layer when $3$D-FilterMap generates the same number of filters. Our work is fundamentally different from the network compression literature that reduces the size of a learned large network in the sense that a small network is directly learned from scratch. Experimental results demonstrate that $3$D-FM-CNN enjoys a small parameter space by learning compact $3$D-FilterMaps, while achieving performance compared to that of the baseline CNNs which learn the same number of filters as that generated by the corresponding $3$D-FilterMap.
Gatekeeping Algorithms with Human Ethical Bias: The ethics of algorithms in archives, libraries and society
Venue: arXiv cs.AI 2018
Year: 2018

In the age of algorithms, I focus on the question of how to ensure algorithms that will take over many of our familiar archival and library tasks, will behave according to human ethical norms that have evolved over many years. I start by characterizing physical archives in the context of related institutions such as libraries and museums. In this setting I analyze how ethical principles, in particular about access to information, have been formalized and communicated in the form of ethical codes, or: codes of conducts. After that I describe two main developments: digitalization, in which physical aspects of the world are turned into digital data, and algorithmization, in which intelligent computer programs turn this data into predictions and decisions. Both affect interactions that were once physical but now digital. In this new setting I survey and analyze the ethical aspects of algorithms and how they shape a vision on the future of archivists and librarians, in the form of algorithmic documentalists, or: codementalists. Finally I outline a general research strategy, called IntERMEeDIUM, to obtain algorithms that obey are human ethical values encoded in code of ethics.
Energy Efficiency Maximization of Full-Duplex Two-Way Relay With Non-Ideal Power Amplifiers and Non-Negligible Circuit Power
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we maximize the energy efficiency (EE) of full-duplex (FD) two-way relay (TWR) systems under non-ideal power amplifiers (PAs) and non-negligible transmission-dependent circuit power. We start with the case where only the relay operates full duplex and two timeslots are required for TWR. Then, we extend to the advanced case, where the relay and the two nodes all operate full duplex, and accomplish TWR in a single timeslot. In both cases, we establish the intrinsic connections between the optimal transmit powers and durations, based on which the original non-convex EE maximization can be convexified and optimally solved. Simulations show the superiority of FD-TWR in terms of EE, especially when traffic demand is high. The simulations also reveal that the maximum EE of FD-TWR is more sensitive to the PA efficiency, than it is to self-cancellation. The full FD design of FD-TWR is susceptible to traffic imbalance, while the design with only the relay operating in the FD mode exhibits strong tolerance.
Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption
Venue: arXiv cs.CV 2018
Year: 2018

Recent advances in vision tasks (e.g., segmentation) highly depend on the availability of large-scale real-world image annotations obtained by cumbersome human labors. Moreover, the perception performance often drops significantly for new scenarios, due to the poor generalization capability of models trained on limited and biased annotations. In this work, we resort to transfer knowledge from automatically rendered scene annotations in virtual-world to facilitate real-world visual tasks. Although virtual-world annotations can be ideally diverse and unlimited, the discrepant data distributions between virtual and real-world make it challenging for knowledge transferring. We thus propose a novel Semantic-aware Grad-GAN (SG-GAN) to perform virtual-to-real domain adaption with the ability of retaining vital semantic information. Beyond the simple holistic color/texture transformation achieved by prior works, SG-GAN successfully personalizes the appearance adaption for each semantic region in order to preserve their key characteristic for better recognition. It presents two main contributions to traditional GANs: 1) a soft gradient-sensitive objective for keeping semantic boundaries; 2) a semantic-aware discriminator for validating the fidelity of personalized adaptions with respect to each semantic region. Qualitative and quantitative experiments demonstrate the superiority of our SG-GAN in scene adaption over state-of-the-art GANs. Further evaluations on semantic segmentation on Cityscapes show using adapted virtual images by SG-GAN dramatically improves segmentation performance than original virtual data. We release our code at https://github.com/Peilun-Li/SG-GAN.
Energy-efficient resource allocation for hybrid bursty services in multi-relay OFDM networks
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we propose Mapped Two-way Water Filling (MTWF) scheme to maximize Energy Efficiency (EE) for hybrid bursty services with Quality of Services (QoS) requirements in Two-Way Multi-Relay (TWMR) OFDM networks. The bursty traffic is first analyzed by strictly proved equivalent homogeneous Poisson process, based on which the QoS requirements are converted into sum-rate constraints. The formulated non-convex EE maximization problem, including subcarrier assignment, Relay Selection (RS) and rate allocation, is NP-hard involving combinatorial optimization. To conduct optimal RS on each subcarrier without priori bursty traffic knowledge, we utilize some approximate relationships under high data rate demands to remove its dependence on two-way data rates, and simplify the whole optimization problem as well. After the optimal channel configuration is obtained, which only depends on channel conditions, subcarrier assignment is attained through Elitist Selection Genetic Algorithm (ESGA), and rate allocation of each service is fulfilled by deducing two-way water filling principle. A new equivalent optimization objective function is proposed next as the simple evaluating index in ESGA to reduce complexity. Finally, simulations are carried out to verify the superiority and convergence of our scheme, as well as the applicability for different scenarios.
A Greedy Search Tree Heuristic for Symbolic Regression
Venue: arXiv cs.AI 2018
Year: 2018

Symbolic Regression tries to find a mathematical expression that describes the relationship of a set of explanatory variables to a measured variable. The main objective is to find a model that minimizes the error and, optionally, that also minimizes the expression size. A smaller expression can be seen as an interpretable model considered a reliable decision model. This is often performed with Genetic Programming which represents their solution as expression trees. The shortcoming of this algorithm lies on this representation that defines a rugged search space and contains expressions of any size and difficulty. These pose as a challenge to find the optimal solution under computational constraints. This paper introduces a new data structure, called Interaction-Transformation (IT), that constrains the search space in order to exclude a region of larger and more complicated expressions. In order to test this data structure, it was also introduced an heuristic called SymTree. The obtained results show evidence that SymTree are capable of obtaining the optimal solution whenever the target function is within the search space of the IT data structure and competitive results when it is not. Overall, the algorithm found a good compromise between accuracy and simplicity for all the generated models.
Design and Implementation of a Polar Codes Blind Detection Scheme
Venue: arXiv cs.IT 2018
Year: 2018

In blind detection, a set of candidates has to be decoded within a strict time constraint, to identify which transmissions are directed at the user equipment. Blind detection is required by the 3GPP LTE/LTE-Advanced standard, and it will be required in the 5th generation wireless communication standard (5G) as well. Polar codes have been selected for use in 5G: thus, the issue of blind detection of polar codes must be addressed. We propose a polar code blind detection scheme where the user ID is transmitted instead of some of the frozen bits. A first, coarse decoding phase helps selecting a subset of candidates that is decoded by a more powerful algorithm: an early stopping criterion is also introduced for the second decoding phase. Simulations results show good missed detection and false alarm rates, along with substantial latency gains thanks to early stopping. We then propose an architecture to implement the devised blind detection scheme, based on a tunable decoder that can be used for both phases. The architecture is synthesized and implementation results are reported for various system parameters. The reported area occupation and latency, obtained in 65 nm CMOS technology, are able to meet 5G requirements, and are guaranteed to meet them with even less resource usage in the latest technology nodes.
Urban Explorations: Analysis of Public Park Usage using Mobile GPS Data
Venue: arXiv cs.CY 2018
Year: 2018

This study analyzes mobile phone data derived from 10 million daily active users across the United States to better understand the spatio-temporal activity patterns of users in Central Park, New York. The aim of this initial investigation is to create quantifiable measures for understanding public space usage in regions of the city that have no natural data source for measuring activity. We analyze the trip behaviors of users across time and different regions in the park to find patterns of co-location and shared time and, thus, potential social interaction. We find that regions with established amenities and points of interest exhibit a higher percentage of shared experiences, indicating that institutional amenities act as 'beacons' for users' experiences in the park.
Tempest: Temporal Dynamics in Anonymity Systems
Venue: arXiv cs.CR 2018
Year: 2018

Many recent proposals for anonymous communication omit from their security analyses a consideration of the effects of time on important system components. In practice, many components of anonymity systems, such as the client location and network structure, exhibit changes and patterns over time. In this paper, we focus on the effect of such temporal dynamics on the security of anonymity networks. We present Tempest, a suite of novel attacks based on (1) client mobility, (2) usage patterns, and (3) changes in the underlying network routing. Using experimental analysis on real-world datasets, we demonstrate that these temporal attacks degrade user privacy across a wide range of anonymity networks, including deployed systems such as Tor; path-selection protocols for Tor such as DeNASA, TAPS, and Counter-RAPTOR; and network-layer anonymity protocols for Internet routing such as Dovetail and HORNET. The degradation is in some cases surprisingly severe. For example, a single host failure or network route change could quickly and with high certainty identify the client's ISP to a malicious host or ISP. The adversary behind each attack is relatively weak - generally passive and in control of one network location or a small number of hosts. Our findings suggest that designers of anonymity systems should rigorously consider the impact of temporal dynamics when analyzing anonymity.
Distance formulas capable of unifying Euclidian space and probability space
Venue: arXiv cs.AI 2018
Year: 2018

For pattern recognition like image recognition, it has become clear that each machine-learning dictionary data actually became data in probability space belonging to Euclidean space. However, the distances in the Euclidean space and the distances in the probability space are separated and ununified when machine learning is introduced in the pattern recognition. There is still a problem that it is impossible to directly calculate an accurate matching relation between the sampling data of the read image and the learned dictionary data. In this research, we focused on the reason why the distance is changed and the extent of change when passing through the probability space from the original Euclidean distance among data belonging to multiple probability spaces containing Euclidean space. By finding the reason of the cause of the distance error and finding the formula expressing the error quantitatively, a possible distance formula to unify Euclidean space and probability space is found. Based on the results of this research, the relationship between machine-learning dictionary data and sampling data was clearly understood for pattern recognition. As a result, the calculation of collation among data and machine-learning to compete mutually between data are cleared, and complicated calculations became unnecessary. Finally, using actual pattern recognition data, experimental demonstration of a possible distance formula to unify Euclidean space and probability space discovered by this research was carried out, and the effectiveness of the result was confirmed.
Domain-Specific Face Synthesis for Video Face Recognition from a Single Sample Per Person
Venue: arXiv cs.CV 2018
Year: 2018

The performance of still-to-video FR systems can decline significantly because faces captured in unconstrained operational domain (OD) over multiple video cameras have a different underlying data distribution compared to faces captured under controlled conditions in the enrollment domain (ED) with a still camera. This is particularly true when individuals are enrolled to the system using a single reference still. To improve the robustness of these systems, it is possible to augment the reference set by generating synthetic faces based on the original still. However, without knowledge of the OD, many synthetic images must be generated to account for all possible capture conditions. FR systems may, therefore, require complex implementations and yield lower accuracy when training on many less relevant images. This paper introduces an algorithm for domain-specific face synthesis (DSFS) that exploits the representative intra-class variation information available from the OD. Prior to operation, a compact set of faces from unknown persons appearing in the OD is selected through clustering in the captured condition space. The domain-specific variations of these face images are projected onto the reference stills by integrating an image-based face relighting technique inside the 3D reconstruction framework. A compact set of synthetic faces is generated that resemble individuals of interest under the capture conditions relevant to the OD. In a particular implementation based on sparse representation classification, the synthetic faces generated with the DSFS are employed to form a cross-domain dictionary that account for structured sparsity. Experimental results reveal that augmenting the reference gallery set of FR systems using the proposed DSFS approach can provide a higher level of accuracy compared to state-of-the-art approaches, with only a moderate increase in its computational complexity.
On decoding procedures of intertwining codes
Venue: arXiv cs.IT 2018
Year: 2018

One of the main weakness of the family of centralizer codes is that its length is always $n^2$. Thus we have taken a new matrix equation code called intertwining code. Specialty of this code is the length of it, which is of the form $nk$. We establish two decoding methods which can be fitted to intertwining codes as well as for any linear codes. We also show an inclusion of linear codes into a special class of intertwining codes.
A Class of MSR Codes for Clustered Distributed Storage
Venue: arXiv cs.IT 2018
Year: 2018

Clustered distributed storage models real data centers where intra- and cross-cluster repair bandwidths are different. In this paper, exact-repair minimum-storage-regenerating (MSR) codes achieving capacity of clustered distributed storage are designed. Focus is given on two cases: $ε=0$ and $ε=1/(n-k)$, where $ε$ is the ratio of the available cross- and intra-cluster repair bandwidths, $n$ is the total number of distributed nodes and $k$ is the number of contact nodes in data retrieval. The former represents the scenario where cross-cluster communication is not allowed, while the latter corresponds to the case of minimum cross-cluster bandwidth that is possible under the minimum storage overhead constraint. For the $ε=0$ case, two types of locally repairable codes are proven to achieve the MSR point. As for $ε=1/(n-k)$, an explicit MSR coding scheme is suggested for the two-cluster situation under the specific condition of $n = 2k$.
Explorations in an English Poetry Corpus: A Neurocognitive Poetics Perspective
Venue: arXiv cs.CL 2018
Year: 2018

This paper describes a corpus of about 3000 English literary texts with about 250 million words extracted from the Gutenberg project that span a range of genres from both fiction and non-fiction written by more than 130 authors (e.g., Darwin, Dickens, Shakespeare). Quantitative Narrative Analysis (QNA) is used to explore a cleaned subcorpus, the Gutenberg English Poetry Corpus (GEPC) which comprises over 100 poetic texts with around 2 million words from about 50 authors (e.g., Keats, Joyce, Wordsworth). Some exemplary QNA studies show author similarities based on latent semantic analysis, significant topics for each author or various text-analytic metrics for George Eliot's poem 'How Lisa Loved the King' and James Joyce's 'Chamber Music', concerning e.g. lexical diversity or sentiment analysis. The GEPC is particularly suited for research in Digital Humanities, Natural Language Processing or Neurocognitive Poetics, e.g. as training and test corpus, or for stimulus development and control.
The Power Allocation Game on A Network: Balanced Equilibrium
Venue: arXiv cs.GT 2018
Year: 2018

This paper studies a special kind of equilibrium termed as "balanced equilibrium" which arises in the power allocation game defined in \cite{allocation}. In equilibrium, each country in antagonism has to use all of its own power to counteract received threats, and the "threats" made to each adversary just balance out the threats received from that adversary. This paper establishes conditions on different types of networked international environments in order for this equilibrium to exist. The paper also links the existence of this type of equilibrium on structurally balanced graphs to the Hall's Maximum Matching problem and the Max Flow problem.